On Internet today, an overabundance of information can be accessed, making it difficult for users to make appropriate choices. This phenomenon is known as information overload. Collaborative Filtering (CF) algorithms are most commonly used technique in Recommender Systems (RS) for Information Filtering, is an answer to the problem of information overload. It makes use of ratings that user have assigned to items which plays an important role in e-commerce to assist users in choosing items of their interest. For large and complex data, Multi-Criteria Collaborative Filtering (MC-CF)frequently give better performance, accurate and high quality recommendations for users considering multiple aspects of item. CF algorithms need to be continuously updated because of a constant increase in the quantity of information, ways of access to that information, scalability and sparseness in rating matrix. Dimensionality Reduction techniques like: Matrix Factorization and Tensor Factorization techniques have proved to be a quite promising solution to the problem of designing efficient CF algorithm in the Big Data Era.
This work aims at offering University Recommendation System to provide University/College Recommendation to Students which combines Multi-Criteria Collaborative Filtering and Dimensionality Reduction technique to provide high quality University/College recommendation to Students. The proposed solution not only reduces the computation cost but also increases the accuracy and efficiency of the MC-CF algorithms implemented using the Apache Mahout framework. By utilizing Mahout API’s, the response time of algorithm were significantly improved.
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